Search method and search device

ABSTRACT

An apparatus receives, via an input device, query input data including a word or a phrase, and acquires search result set data using the query input data. The apparatus acquires, for a value indicating a strength of a relationship between each impression word included in an impression word group and each word included in the query input data, and extracts the first feature word group according to the value indicating the strength of the relationship with each word, from the impression word group. The apparatus displays the search result set data using the first feature word group as an item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-91663, filed on May 10, 2018,the Japanese Patent Application No. 2017-199753, filed on Oct. 13, 2017,and the Japanese Patent Application No. 2018-2822, filed on Jan. 11,2018, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related to a search method and asearch device.

BACKGROUND

A system that displays search results based on a query input by a useron a radar chart is known in the related art. In the system of therelated art, for example, when the radar chart is displayed, it is knownthat axis items (word) are extracted from a set of search results basedon the query input by the user.

Japanese Laid-open Patent Publication No. 2008-003869 is an example ofthe related art.

SUMMARY

According to an aspect of the embodiments, an apparatus receives, via aninput device, query input data including a word or a phrase, andacquires search result set data using the query input data. Theapparatus acquires, for a value indicating a strength of a relationshipbetween each impression word included in an impression word group andeach word included in the query input data, and extracts the firstfeature word group according to the value indicating the strength of therelationship with each word, from the impression word group. Theapparatus displays the search result set data using the first featureword group as an item.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram that illustrates an example of a systemconfiguration of a search system of a first embodiment;

FIG. 2 is a diagram that illustrates an example of a hardwareconfiguration of a search device of the first embodiment;

FIG. 3 is a diagram that describes functions of the search device of thefirst embodiment;

FIG. 4 is a flowchart that describes processing by an index generationunit;

FIGS. 5A and 5B are example tables of index information generated by theindex generation unit;

FIG. 6 is an example table of an impression word map of the firstembodiment;

FIG. 7 is a flowchart that describes processing by an impression spacelearning unit;

FIG. 8 is an example table of an impression word score list of the firstembodiment;

FIG. 9 is a flowchart that describes processing of a search processingunit of the first embodiment;

FIG. 10 is a flowchart that describes processing of an impression wordscore output unit and an axis determination unit of the firstembodiment;

FIG. 11 is a diagram describing processing of the impression word scoreoutput unit and the axis determination unit;

FIG. 12 is a first diagram illustrating a display example of a radarchart of the first embodiment;

FIG. 13 is a diagram illustrating an example of a radar chart as acomparative example;

FIG. 14 is a second diagram that illustrates a display example of aradar chart of the first embodiment;

FIG. 15 is a diagram describing functions of a search device of a secondembodiment;

FIG. 16 is a flowchart that describes processing of a search processingunit of the second embodiment;

FIG. 17 is a flowchart describing processing of an axis determinationunit of the second embodiment;

FIGS. 18A and 18B are graphs describing variation in scores in animpression word map of a third embodiment;

FIG. 19 is a diagram describing functions of a search device of thethird embodiment;

FIG. 20 is a table illustrating an example of the impression word map ofthe third embodiment;

FIG. 21 is an example table of an impression word score list of thethird embodiment;

FIG. 22 is a flowchart describing processing of a search processing unitof the third embodiment;

FIG. 23 is a flowchart describing processing of an impression word scoreoutput unit and an axis determination unit of the third embodiment;

FIG. 24 is a diagram describing extraction of a candidate axis by thesearch processing unit of the third embodiment;

FIG. 25 is a first diagram illustrating a display example of the thirdembodiment;

FIG. 26 is a second diagram illustrating a display example of the thirdembodiment;

FIG. 27 is a diagram describing functions of a search device of a fourthembodiment;

FIG. 28 is a flowchart describing axis change processing in the fourthembodiment;

FIGS. 29A to 29C are diagrams describing change of axis;

FIG. 30 is a diagram describing functions of a search device of a fifthembodiment;

FIG. 31 is a flowchart describing processing of a correction listgeneration unit of the fifth embodiment;

FIG. 32 is a diagram illustrating an example of a correction list of thefifth embodiment;

FIG. 33 is a flowchart describing processing of an impression word scoreoutput unit, an axis determination unit, and a score correction unit ofthe fifth embodiment;

FIG. 34 is a diagram describing an outline of a sixth embodiment;

FIGS. 35A and 35B are diagrams describing change in score of impressionwords;

FIG. 36 is a diagram describing functions of a search device of thesixth embodiment;

FIG. 37 is a first flowchart describing processing of a searchprocessing unit of the sixth embodiment;

FIG. 38 is a second flowchart describing processing of the searchprocessing unit of the sixth embodiment;

FIG. 39 is a third flowchart describing processing of the searchprocessing unit of the sixth embodiment;

FIGS. 40A and 40B are diagrams describing correspondence between a scoreand a slider value of the sixth embodiment;

FIG. 41 is an example correspondence table of the sixth embodiment;

FIG. 42 is a diagram illustrating a display example of the sixthembodiment;

FIGS. 43A and 43B are diagrams illustrating another display example ofthe sixth embodiment;

FIG. 44 is a graph describing correspondence between a score and aslider value of a seventh embodiment;

FIG. 45 is a flowchart describing processing of a search processing unitof the seventh embodiment; and

FIG. 46 is an example correspondence table of the seventh embodiment.

DESCRIPTION OF EMBODIMENTS

In the related art, since the axis words of the radar chart areextracted from the set of search results, there may be a situation inwhich the correspondence between the query and axes is not clear despitethe fact that the radar chart is displayed with appropriate axes,depending on the query input by a user. In such a situation, it isdifficult for the user to promptly evaluate whether or not the searchresults meet the intention of the user.

It is desirable to properly evaluate search results.

First Embodiment

Hereinafter, the first embodiment will be described with reference tothe drawings. FIG. 1 is a diagram that illustrates an example of asystem configuration of a search system of a first embodiment.

A search system 100 of the present embodiment includes a search device200 and a terminal device 300. The search device 200 and the terminaldevice 300 are connected via a network. The search device 200 of thepresent embodiment communicates with a search target document database400 in which information to be searched by the search device 200 isstored. The information stored in the search target document database400 of the present embodiment is, for example, text data such asdocument data.

The search target document database 400 of the present embodiment mayinclude, for example, a plurality of databases. The search targetdocument database 400 of the present embodiment may be included in thesearch system 100.

When a query is input by a user in the terminal device 300, the searchdevice 200 of the present embodiment searches the search target documentdatabase 400 based on the input query. The search device 200 causes theterminal device 300 to display the search result acquired as a result ofthe search processing by a radar chart. The query indicates, forexample, a word or a phrase (compound word) input when the user of theterminal device 300 or the search device 200 performs a search request.

At this time, the search device 200 of the present embodiment displays aradar chart with words extracted based on the query input from theterminal device 300 as axes.

In the present embodiment, it is possible to make the correspondencebetween the query and the axis of the radar chart clear by using thewords extracted based on the query input by the user as axes of theradar chart. Therefore, in the present embodiment, it is possible toallow the user to easily evaluate whether or not the search resultmatches the intention of the user. According to the present embodiment,it is possible to properly evaluate the search result.

The search device 200 of the present embodiment includes a search targetindex storage unit 210, an impression word map storage unit 220, asearch target score storage unit 230, and a search processing unit 240.

The search target index storage unit 210 stores index information inwhich each document data stored in the search target document database400 is associated with words included in each document data. Theimpression word map storage unit 220 stores an impression word mapindicating a relationship between a word and an impression word. Thesearch target score storage unit 230 stores an impression word scorelist indicating the relationship between the words and impression wordsincluded in each document data on the document data included in thesearch target document database 400. Details of each storage unit willbe described later.

When the search processing unit 240 receives input of a query from theterminal device 300, the search processing unit 240 searches the searchtarget document database 400 with reference to the search target indexstorage unit 210, and acquires document data as a search result. In thefollowing description, the search result acquired by the searchprocessing of the search processing unit 240 is referred to as searchresult set data.

The search processing unit 240 selects impression words each having astrong relationship with the content of the query as the axes of theradar chart with reference to the impression word map storage unit 220,and causes the terminal device 300 to display the radar chart for eachsearch result set data.

In the example of FIG. 1, the query is input from the terminal device300 to the search device 200, and the radar chart is displayed on theterminal device 300, but not limited thereto. The query may be input byan input device of the search device 200, and the radar chart may bedisplayed on an output device (display) or the like of the search device200.

Hereinafter, the search device 200 of the present embodiment will bedescribed. FIG. 2 is a diagram that illustrates an example of a hardwareconfiguration of a search device of the first embodiment.

The search device 200 of the present embodiment is an informationprocessing device including an input device 21, an output device 22, adrive device 23, an auxiliary storage device 24, a memory device 25, anarithmetic processing device 26, and an interface device 27 mutuallyconnected via a bus B.

The input device 21 is a device to input various kinds of information,and is realized by, for example, a keyboard, a pointing device, or thelike. The output device 22 is a device to output various kinds ofinformation, and is realized by, for example, a display or the like. Theinterface device 27 includes a LAN card or the like, and is used to beconnected to a network.

A search program is at least a part of various programs that control thesearch device 200. The search program is provided by, for example,distribution of a storage medium 28, download from a network, or thelike. Various types of storage media may be used as the storage medium28 in which the search program is recorded; a storage medium thatoptically, electrically, or magnetically records information, such as aCD-ROM, a flexible disk, or a magneto-optical disk, a semiconductormemory that electrically records information, such as a ROM or a flashmemory, or the like.

When the storage medium 28 in which the search program is recorded isset in the drive device 23, the search program is installed from thestorage medium 28 to the auxiliary storage device 24 via the drivedevice 23. The search program downloaded from the network is installedin the auxiliary storage device 24 via the interface device 27.

The auxiliary storage device 24 stores significant files, data, and thelike while storing the installed search program. The memory device 25reads and stores the search program from the auxiliary storage device 24when the search device 200 is activated. The arithmetic processingdevice 26 realizes various types of processing as described lateraccording to the search program stored in the memory device 25.

The hardware configuration of the terminal device 300 of the presentembodiment is the same as that of the search device 200, and descriptionthereof is omitted. The terminal device 300 may be, for example, atablet type terminal device, a smart phone, or the like, and may have adisplay operation device realized by a touch panel or the like insteadof the input device 21 and the output device 22.

Next, with reference to FIG. 3, the functions of the search device 200of the present embodiment will be described. FIG. 3 is a diagram thatdescribes the functions of the search device of the first embodiment.

The search device 200 of the present embodiment includes the searchtarget index storage unit 210, the impression word map storage unit 220,the search target score storage unit 230, and the search processing unit240.

Index information 211 is stored in the search target index storage unit210. The index information 211 of the present embodiment is created bypreliminary processing by an index generation unit 247 described later.

An impression word map 221 is stored in the impression word map storageunit 220. The impression word map 221 of the present embodiment is givenin advance by a manager or the like of the search device 200.

An impression word score list 231 is stored in the search target scorestorage unit 230. The impression word score list 231 is created byprocessing of the search processing unit 240 described later.

The search processing unit 240 of the present embodiment includes aninput receiving unit 241, an analysis unit 242, a search unit 243, animpression word score output unit 244, an axis determination unit 245,an output unit 246, the index generation unit 247, and an impressionspace learning unit 248. Each unit of the search processing unit 240 isrealized by the arithmetic processing device 26 of the search device 200reading and executing the search program stored in the memory device 25or the like.

The input receiving unit 241 of the present embodiment receives variousinputs to the search device 200. For example, the input receiving unit241 receives input of the query to the search device 200.

The analysis unit 242 of the present embodiment performs wordsegmentation, modification analysis, meaning analysis, and the like tokeywords and natural sentences. For example, the analysis unit 242specifies a search word group based on the query, or specifies a wordfrom the document data of the search result acquired from the searchtarget document database 400. For example, the analysis unit 242 of thepresent embodiment fulfills the function of a specification unit thatspecifies a search word group from the query.

The search unit 243 of the present embodiment refers to the searchtarget index storage unit 210, specifies the document data including thesearch word group acquired from the analysis unit 242, and acquires thesearch result set data from the search target document database 400.

The impression word score output unit 244 converts each word included inthe search word group acquired from the query to a vector by adistributed representation. The impression word score output unit 244outputs a score indicating strength of a relationship between the searchword group and the impression words included in the impression word map221 based on each word converted to a vector and a conversion model 250described later.

For example, the impression word score output unit 244 outputs the scoreindicating the strength of the relationship between the word representedin a vector as an input to the conversion model 250 and each impressionword included in the impression word map 221.

A method based on the distributed representation of words of the presentembodiment is a method that estimates a semantic similarity relationship(similarity) between each word using a large amount of learning datagiven separately, and represents meaning of the word by a vector bydisposing each word in a vector space of a predetermined number ofdimensions so as to be consistent with the estimated inter-wordsimilarity. In the following description, the vector representing themeaning of a word converted by the distributed representation of wordsis referred to as a semantic vector.

The impression word of the present embodiment is a word that remindsimpression of the search result such as an adjective, an adjective verb,and an adverb.

The axis determination unit 245 determines the impression words as theaxes of the radar chart to display the search result according to thescore of each word output by the impression word score output unit 244.The axis of the radar chart is, for example, one of the items to displaythe search result set data.

The output unit 246 of the present embodiment displays the radar chartof the search result set data using the impression words determined bythe axis determination unit 245 as the axes.

For example, the output unit 246 of the present embodiment displays theimpression words extracted according to the strength of the relationshipwith the search word group as the item when displaying the search resultset data acquired by search processing by the search word group based onthe query.

The index generation unit 247 of the present embodiment creates theindex information 211 stored in the search target index storage unit 210from the search target document database 400.

The impression space learning unit 248 outputs the conversion model 250based on the search target document data stored in the search targetdocument database 400 and the impression word map 221 stored in theimpression word map storage unit 220.

The processing of the index generation unit 247 and the impression spacelearning unit 248 of the present embodiment may be performed in advanceas the preliminary processing of search processing performed after thequery is input.

The preliminary processing of this embodiment will be described below.First, generation of the index information 211 by the index generationunit 247 will be described with reference to FIGS. 4 to 58.

FIG. 4 is a flowchart that describes the processing by an indexgeneration unit.

The index generation unit 247 of the present embodiment refers to thesearch target document database 400, and determines whether or not acertain word is included in the document data for each document datastored in the search target document database 400 (step S401).

In the present embodiment, a document ID is included in each documentdata stored in the search target document database 400 as identificationinformation to specify the document data.

The index generation unit 247 acquires a set of document data includingthe word, generates the index information 211 in which the word, thenumber of acquired document data pieces, and the document ID as theidentification information that specifies the document data areassociated with each other for each word (step S402), and ends theprocessing.

FIGS. 5A and 5B are example tables of index information generated by theindex generation unit. FIG. 5A is a table illustrating intermediate dataacquired as a result of processing in step S401. FIG. 5B is an exampletable of the index information 211.

The index generation unit 247 of the present embodiment includes, forexample, “this”, “pen”, and “apple” in the document data.

In this case, the index generation unit 247 determines whether or noteach word of “this”, “pen”, and “apple” is included in the document datafor each document data stored in the search target document database400, and stores intermediate data 205 indicating the determinationresult.

In the intermediate data 205 illustrated in FIG. 5A, it is found thatthe word “this” is included in the document data with document ID “1”,the document data with document ID “2”, and the document data withdocument ID “N”. It is found that the word “pen” is included in thedocument data with the document ID “1”. It is found that the word“apple” is included in the document data with the document ID “2”.

Next, the index generation unit 247 generates the index information 211in which the number of document data pieces including the word isassociated with the document ID for each word.

The index information 211 illustrated in FIG. 5B includes keys, thenumber of match documents, and an array of documents as items ofinformation. The value of the item “key” indicates the word estimated tobe included in the document data. The value of the item “number of matchdocuments” indicates the number of document data pieces including wordsas the keys. The value of the item “array of documents” indicates thedocument ID of the document data including the words as the keys. Thenumber of the document IDs included in the value of the item “array ofdocuments” is equal to the number as the value of the item “number ofmatch documents”.

In the index information 211 of FIG. 5B, the number of document datapieces including the word “this” is 239, and the document ID of thedocument data including the word “this” is document ID “1”, “2”, . . . ,“N”.

As described above, the index generation unit 247 of the presentembodiment generates the index information 211, and stores theinformation in the search target index storage unit 210.

Next, before describing the processing by the impression space learningunit 248, the impression word map 221 stored in the impression word mapstorage unit 220 of the present embodiment will be described.

FIG. 6 is an example table of an impression word map of the firstembodiment. The impression word map 221 of the present embodiment is,for example, created by a manager or the like of the search system 100,and given to the search device 200 in advance. For the impression wordmap 221, for example, a map that is created based on a result of aquestionnaire or the like conducted on a large number of people toanswer the strength of the relationship between words and impressionwords may be given to the search device 200.

In the impression word map 221 of the present embodiment, a score(value) indicating the strength of the relationship between theimpression word prepared in advance and each word is associated witheach word. The word included in the impression word map 221 may be anyword estimated to be included in the document data stored in the searchtarget document database 400.

The score of the present embodiment is, for example, a value from 0 to10. In a case where the score is “0”, it indicates that there is norelationship between the word and the impression word, and the largerthe score, the stronger the relationship between the word and theimpression word.

In the impression word map 221 illustrated in FIG. 6, “premier”,“lively”, “hideaway”, “quiet”, “healthy”, and the like are listed as animpression word.

For example, in the impression word map 221, the score indicating thestrength of the relationship between a word “date” and the impressionword “premier” is “9.21”, and the score indicating the strength of therelationship between the word “date” and the impression word “lively” is“3.23”.

Therefore, it is found that the word “date” has a stronger relationshipwith the impression word “premier” than the impression word “lively”.

Next, the processing of the impression space learning unit 248 of thepresent embodiment will be described with reference to FIG. 7. FIG. 7 isa flowchart that describes the processing by an impression spacelearning unit.

The impression space learning unit 248 of the present embodiment refersto the search target document database 400, inputs each document dataincluded in the search target document database 400, and learns thedistributed representations of words included in each document data withunsupervised learning (step S701). For example, the impression spacelearning unit 248 internally stores the semantic vector of wordsincluded in each document data.

The impression space learning unit 248 refers to the impression word map221, and learns the score corresponding to the impression words includedin the impression word map 221 with respect to the distributedrepresentation of each word with supervised learning (step S702). Forexample, the impression space learning unit 248 refers to the impressionword map 221 to machine learn the relationship between the semanticvector of each word and the score indicating the strength of therelationship with each impression word associated with the word.

The impression space learning unit 248 outputs and stores the learnedresult as the conversion model 250 (step S703). For example, theconversion model 250 of the present embodiment is a model in which asemantic vector of a word acquired by the distributed representation isinput, and the score for each impression word associated with the wordis output.

The described-above is the preliminary processing by the indexgeneration unit 247 and preliminary processing by the impression spacelearning unit 248.

Next, the impression word score list 231 stored in the search targetscore storage unit 230 of the present embodiment will be described withreference to FIG. 8.

FIG. 8 is an example table of an impression word score list. Theimpression word score list 231 of the present embodiment is, forexample, a list acquired by processing of the analysis unit 242 and theimpression word score output unit 244 of the search processing unit 240.The impression word score list 231 of the present embodiment is referredto when the search result by the search unit 243 is displayed as theradar chart by the output unit 246.

The impression word score list 231 indicates the score indicating thestrength of the relationship between the document ID of document dataincluded in the search target document database 400 and each impressionword included in the impression word score list 231. The impressionwords included in the impression word score list 231 are the same as theimpression words included in the impression word map 221.

For example, the search processing unit 240 of the present embodimentextracts a word from the document data stored in the search targetdocument database 400 by the analysis unit 242. The search processingunit 240 inputs the extracted word into the conversion model 250 afterthe extracted word is converted to a semantic vector by the impressionword score output unit 244, and acquires the score indicating thestrength of the relationship between each impression word and each word.

In the impression word score list 231 of the present embodiment, thescore may be a score of each impression word associated with thedocument ID.

In this way, for example, in a case where a single word is extractedfrom the document data stored in the search target document database400, the score indicating the strength of the relationship between theword and each impression word becomes the score indicating the strengthof the relationship between the document data and each impression word.

For example, in a case where a plurality of words are extracted from thedocument data, the score indicating the strength of the relationshipbetween each impression word and each word is output for the pluralityof words. Therefore, in the present embodiment, for each impressionword, a maximum score or an average score of the plurality of words maybe acquired, and the maximum score or the average score of eachimpression word may be the score indicating the strength of therelationship between the document data and each impression word.

For example, it is assumed that two words “date” and “delicious” areextracted from the document data. In this case, the search processingunit 240 acquires the score indicating the strength of the relationshipbetween the word “date” and each impression word included in theimpression word map 221, and the score indicating the strength of therelationship between the word “delicious” and each impression wordincluded in the impression word map 221.

The search processing unit 240 acquires the maximum value of the scorecorresponding to the word “date” and the score corresponding to the word“delicious” for each impression word. For example, it is assumed thatthe score indicating the strength of the relationship between the word“date” and the impression word “premier” is 9.21, and the scoreindicating the strength of the relationship between the word “delicious”and the impression word “premier” is 9.38. In this case, the searchprocessing unit 240 may take the maximum value 9.38 between 9.21 and9.38 as the score indicating the strength of the relationship betweenthe document data and the impression word “premier”.

Next, the processing of the search processing unit 240 of the presentembodiment will be described with reference to FIG. 9. FIG. 9 is aflowchart that describes the processing of a search processing unit ofthe first embodiment.

When the input receiving unit 241 receives an input of a query, thesearch processing unit 240 of the present embodiment acquires a searchword group by morphological analysis or the like from the query by theanalysis unit 242, searches the index information 211 with the searchword group by the search unit 243, and specifies the document IDcorresponding to the search word group (step S901). In the presentembodiment, the document data indicated by the document ID specified instep S901 is the search result set data.

The search processing unit 240 extracts the n number of impression wordswith clear relationship with the query as the axis based on the inputquery by the impression word score output unit 244, and sets the nnumber of impression words as an α axis by the axis determination unit245 (step S902). The axis in step S902 is an axis of the radar chart,and is one of the items to be an attribute of the search result setdata. Details of processing of step S902 will be described later. The αaxis of the present embodiment is an axis in which an impression wordthat has a large score indicating the strength of the relationship withthe query, has a strong relationship with the query, and thecorrespondence is clear is set.

The search processing unit 240 extracts the m number of preset words bythe axis determination unit 245, and sets them as a β axis (step S903).The preset word may be, for example, selected and set from theimpression words included in the impression word map 221. The presetword may be selected and set from the words included in the indexinformation 211. For example, the preset word may be a word included inthe search result set data.

The β axis of the present embodiment indicates an axis other than the αaxis among all of the axes in the radar chart. For example, the β axisis an axis in which an impression word not strongly related to the queryis set compared to the α axis.

The output unit 246 uses the α axis and the β axis as an output axis(step S904), draws the search result set data indicated by the documentID acquired by the search unit 243 in step S901 by the output unit 246on a radar chart (step S905), and ends the processing.

Next, the processing of step S902 of FIG. 9 will be described withreference to FIG. 10. FIG. 10 is a flowchart that describes theprocessing of an impression word score output unit and an axisdetermination unit of the first embodiment. The processing in FIG. 10indicates the processing of the impression word score output unit 244and the axis determination unit 245.

The impression word score output unit 244 of the present embodimentconverts each word included in the search word group acquired in stepS901 to the distributed representations (step S1001). For example, theimpression word score output unit 244 converts each word included in thesearch word group to a semantic vector by the distributedrepresentation.

The impression word score output unit 244 refers to the conversion model250, and acquires the score indicating the strength of the relationshipbetween each impression word and each word (step S1002). For example,the impression word score output unit 244 inputs the semantic vector ofeach word acquired in step S1001 to the conversion model 250, andacquires the score of each impression word for each word output from theconversion model 250.

The axis determination unit 245 acquires the maximum score of eachimpression word corresponding to each word (step S1003).

The axis determination unit 245 extracts the impression wordscorresponding to the top n maximum scores of each impression word as theα axis related to the query (step S1004). For example, the axisdetermination unit 245 extracts the n number of impression words indescending order of the impression words having the maximum score foreach impression word, and set the n impression words as the α axis.

Hereinafter, the processing of the impression word score output unit 244and the axis determination unit 245 of the present embodiment will bedescribed in details with reference to FIG. 11. FIG. 11 is a diagramdescribing processing of the impression word score output unit and theaxis determination unit.

In FIG. 11, the query input by the user will be described as “quietpremier restaurant”.

The search processing unit 240 of the present embodiment acquires asearch word group 111 by the analysis unit 242 when the input of thequery is received. In the search word group, the word “quiet”,“premier”, are “restaurant” included.

Next, the search processing unit 240 acquires a semantic vector 112 foreach word included in the search word group 111 by the impression wordscore output unit 244.

In the semantic vector 112 illustrated in FIG. 11, each word “quiet”,“premier”, and “restaurant” are indicated as a vector.

When the semantic vector 112 is acquired, the impression word scoreoutput unit 244 inputs the semantic vector 112 to the conversion model250, and acquires the score of each impression word included in theimpression word map 221 for each word. In FIG. 11, the information inwhich each word included in the search word group is associated with thescore of each impression word is referred to as an impression word score113 for each word.

Next, the axis determination unit 245 acquires the maximum score foreach impression word from the impression word score 113 for each word.

For example, in the impression word score 113 for each word, withrespect to the impression word “premier”, the score indicating thestrength of the relationship with the word “quiet” is 3.49, the scoreindicating the strength of the relationship with the word “premier” is10.00 (full marks), and the score indicating the strength of therelationship with the word “restaurant” is 7.95.

Therefore, the axis determination unit 245 acquires “10.00” as themaximum score of the impression word “premier”.

In the impression word score 113 for each word, with respect to theimpression word “healthy”, the score indicating the strength of therelationship with the word “quiet” is 3.48, the score indicating thestrength of the relationship with the word “premier” is 8.33, and thescore indicating the strength of the relationship with the word“restaurant” is 6.72.

Therefore, the axis determination unit 245 acquires “8.33” as themaximum score of the impression word “healthy”.

In the impression word score 113 for each word, with respect to theimpression word “hideaway”, the score indicating the strength of therelationship with the word “quiet” is 9.38, the score indicating thestrength of the relationship with the word “premier” is 6.23, and thescore indicating the strength of the relationship with the word“restaurant” is 3.46.

Therefore, the axis determination unit 245 acquires “9.38” as themaximum score of the impression word “hideaway”.

The axis determination unit 245 of the present embodiment may store themaximum score acquired for each impression word as an impression wordscore list 114 corresponding to the query. The impression word scorelist 114 may be referred to, for example, when displaying the radarchart described later.

The axis determination unit 245 extracts the n number of impressionwords in descending order of the impression words having the maximumscore in the impression word score list 114, and set the n impressionwords as the α axis. In the example of FIG. 11, n is 2.

In the impression word score list 114, the impression word having themaximum score is “premier”, and the impression word having the nextmaximum score is “hideaway”.

Therefore, the axis determination unit 245 of the present embodimentdetermines the impression word “premier” and the impression word“hideaway” as the α axis of the radar chart. For example, the axisdetermination unit 245 sets the impression word “premier” and theimpression word “hideaway” as the items when displaying the searchresult set data.

In this way, in the present embodiment, the impression word having astrong relationship with the query is extracted based on the search wordgroup acquired from the input query. For example, the impression wordextracted here may be a feature word group (hereinafter, also referredto as first feature word group) indicating the feature of the contentestimated from the query. Therefore, the axis determination unit 245 ofthe present embodiment fulfills the function of an extraction unit thatextracts the first feature word group from the query.

When the impression word score 113 for each word is acquired, the axisdetermination unit 245 of the present embodiment determines theimpression word as the α axis according to the maximum score for eachimpression word, but not limited thereto. For example, when theimpression word score 113 for each word is acquired, the axisdetermination unit 245 may extract the n number of impression words indescending order of values obtained by normalizing the score of eachimpression word. When the impression word score 113 for each word isacquired, the axis determination unit 245 may extract the n number ofimpression words in descending order of the average score for eachimpression word.

For example, in the impression word score list 114 of the presentembodiment, the value associated with each impression word is notlimited to the maximum score for each impression word in the impressionword score 113 for each word. In the impression word score list 114, thevalue associated with each impression word may be a value obtained bynormalizing the score of each impression word in the impression wordscore 113 for each word, or may be an average score of each impressionword in the impression word score 113 for each word.

In the present embodiment, the impression word corresponding with thescore is extracted as the α axis, but not limited thereto.

The axis determination unit 245 may extract, for example, the wordhaving the maximum score indicating the strength of the relationshipwith the impression word as the α axis.

For example, the axis determination unit 245 extracts the impressionword “premier” and “hideaway” in descending order of the score of eachimpression word. At this time, the axis determination unit 245 mayextract the word “quiet” having the maximum score indicating thestrength of the relationship with the impression word “hideaway” as theα axis in the impression word score 113 for each word.

In this way, in the present embodiment, the semantic vector of the wordis acquired from the search word group included in the query input bythe user, and the semantic vector is input to the conversion model 250.In the present embodiment, the score indicating the strength of therelationship between each of impression words and each word included inthe search word group is acquired as an output of the conversion model250.

Therefore, in the present embodiment, even if a new word other than thewords prepared in advance is included in the query, it is possible toacquire the score indicating the strength of the relationship betweenthe new word and the impression words. For this reason, according to thepresent embodiment, it is possible to display the search result using animpression word (item) that has a strong relationship with the query andthe correspondence is clear with respect to any queries input by theuser.

Next, the display of the radar chart by the search processing unit 240of the present embodiment will be described with reference to FIGS. 12to 14.

FIG. 12 is a first diagram illustrating a display example of a radarchart of the first embodiment. A screen 121 illustrated in FIG. 12displays a radar chart of “AA restaurant” acquired as the search resultset data in a case where the input query is “quiet premier restaurant”.

In the example of FIG. 12, only “AA restaurant” is displayed as anexample of the search result set data, but not limited thereto. Assearch result set data, in a case where a plurality of search resultsare acquired, a radar chart for each search result may be displayed onthe screen 121.

In the example of FIG. 12, the search result set data may be documentdata indicating a description of “AA restaurant” or the like, and thedescription of “AA restaurant” may be displayed on a search resultdisplay field 123.

In the present embodiment, a rank may be given to each search resultincluded in the search result set data. For example, in the presentembodiment, total points of scores for each axis of the radar chart oran area of a polygon illustrated as a graph may be calculated as a valueindicating the strength of the relationship between the query and thesearch result set data for each search result set data, and the rank maybe given to the search result set data in descending order of thevalues.

For example, in the present embodiment, the rank is given to the searchresult set data in the descending order of the strength of therelationship with the query. In the present embodiment, by assigning therank in this way, it is possible to present the search results indescending order of the strength of the relationship with the query tothe user.

The screen 121 of the present embodiment includes a query display field122, the search result display field 123, and a search result displayfield 124.

The query input by the user is displayed in the query display field 122.A radar chart 125 that corresponds to the search result included in thesearch result set data is displayed in the search result display field123. In the radar chart 125, a graph 126 that indicates the relationshipbetween the search result set data and the axis item of the radar chart125 are displayed. A bar graph 127 that indicates the value of each axisof the radar chart 125 is displayed in the search result display field124.

The radar chart 125 of the present embodiment has five axes, an α1 axis,an α2 axis, a β1 axis, a β2 axis, and a β3 axis. For example, the radarchart 125 displays the search result of “AA restaurant” using fiveitems, two impression words extracted based on the query, and threeimpression words other than the impression words selected based on thequery.

The item corresponding to the α1 axis is “hideaway”, and the itemcorresponding to the α2 axis is “premier”. The item corresponding to theβ1 axis is “calm”, the item corresponding to the β2 axis is “plentiful”,and the item corresponding to the β3 axis is “healthy”.

The items corresponding to the α1 axis and the α2 axis are theimpression words extracted in step S902 of FIG. 9. For example, theitems corresponding to the α1 axis and the α2 axis are impression wordshaving clear relationship with the query.

The items corresponding to the β1 axis, the β2 axis, and the β3 axis arethe words set in step S903 of FIG. 9. For example, the itemscorresponding to the β1 axis, the β2 axis, and the β3 axis are presetwords.

In the example of FIG. 12, the items corresponding to the β1 axis, theβ2 axis, and the β3 axis are impression words selected from theimpression word map 221, but not limited thereto.

The items corresponding to the β1 axis, the β2 axis, and the β3 axis maybe, for example, a word selected from the index information 211. Forexample, the items corresponding to the β1 axis, the β2 axis, and the β3axis may be words extracted from the search result set data. The wordextracted from the search result set data may be a feature word group(second feature word group) indicating the feature of the search resultset data.

Therefore, in the radar chart 125 of the present embodiment, the searchresult set data is displayed using the feature word group extracted fromthe query and the feature word group extracted from the search resultset data as items.

The display of the graph 126 by the output unit 246 after the itemcorresponding to each axis of the radar chart 125 are determined in thesearch processing unit 240 of the present embodiment will be described.

The output unit 246 of the present embodiment refers to the impressionword score list 231 when the item corresponding to each axis of theradar chart 125 is determined.

The output unit 246 acquires the score corresponding to the impressionword set as an item among the scores for each impression wordcorresponding to the document ID of the document data acquired as thesearch result set data in the impression word score list 231, and setsthe score as the value of an item in the radar chart.

For example, it is assumed that the acquired document data as searchresult set data is document data with document ID “1”.

In this case, the output unit 246 refers to the impression word scorelist 231, acquires score “9.2” corresponding to “hideaway” which is anitem corresponding to the α1 axis among the scores of each impressionword corresponding to the document ID “1”, and sets the score as thevalue of the α1 axis of the radar chart 125. The output unit 246acquires score “9.0” corresponding to “premier” which is an itemcorresponding to the α2 axis among the scores for each impression wordcorresponding to the document ID “1”, and set the score as the value ofthe α2 axis in the radar chart 125.

Similarly, the output unit 246 of the present embodiment acquires thevalue of the β1 axis to β3 axis from the impression word score list 231.The output unit 246 displays the graph 126 based on the value of eachaxis.

The impression word score list 231 of the present embodiment may begenerated by preliminary processing. When the search result set data isacquired from the search by the search unit 243, the impression wordscore list 231 may acquire the score for each impression word from thesearch result set data and store the score in the impression word scorelist 231.

In a case where the score for each impression word is acquired from thesearch result set data, the search processing unit 240 may performprocessing from step S1001 to step S1003 of FIG. 10 after acquiring aword group from the search result set data by the analysis unit 242. Thesearch processing unit 240 may store the score for each impression wordwhich is the result of performing the processing in the impression wordscore list 231 associated with the document ID indicating the searchresult set data.

As described above, in the present embodiment, it is possible to allowthe user to easily determine that the search result is what the userintended by setting the impression word with a strong relationship withthe query extracted based on the query as the axis of the radar chart125. Therefore, according to the present embodiment, it is possible toprovide the user of the search system 100 with satisfaction to thesearch result.

Hereinafter, a comparative example of a case that the present embodimentis not applied will be described with reference to with reference toFIG. 13. FIG. 13 is a diagram illustrating an example of a radar chartas a comparative example.

A query display field 132 in which an input query is displayed, a searchresult display field 133 in which the search result is displayed, and aradar chart 134 are displayed on a screen 131 illustrated in FIG. 13.

The items corresponding to the axes 1 to 5 illustrated in the radarchart 134 illustrated in FIG. 13 are words selected from the documentdata associated with “AA restaurant” acquired as the search result.

For example, the item corresponding to the axis 1 is “cheap”, the itemcorresponding to the axis 2 is “feeling of freedom”, the itemcorresponding to the axis 3 is “private room available”, the itemcorresponding to the axis 4 is “lively”, and the item corresponding tothe axis 5 is “plentiful”.

The query input with respect to the items corresponding to the axes 1 to5 is “quiet premier restaurant”.

For this reason, for example, the item “lively” corresponding to theaxis 4 is an item hard to associate from the query, and thecorrespondence with the query is not clear.

As described above, in the radar chart indicating the search result, ina case where a word hard to associate from the query input by the useris used as an item, it is not easy for the user to determine whether ornot the search result meet the intention of the user, and the user maynot feel satisfaction with respect to the search result.

On the other hand, in the present embodiment, it is possible to properlyevaluate the search result since the search result set data is displayedby the radar chart using the impression word, as the item, with a strongrelationship with the query extracted based on the query.

Next, a display example of the radar chart of the present embodimentwill be described with reference to FIG. 14. FIG. 14 is a second diagramthat illustrates a display example of a radar chart of the firstembodiment.

A screen 121A illustrated in FIG. 14 includes a search result displayfield 123A and a search result display field 124A.

The graph 126 and a graph 128 are displayed in a radar chart 125Adisplayed in the search result display field 123A.

The graph 128 is a graph indicating the relationship between the queryand the axis items. The graph 128 may be displayed, for example, withreference to the impression word score list 114 acquired by theprocessing that extracts items corresponding to an α1 axis and an α2axis of the radar chart 125A (see FIG. 11).

For example, the output unit 246 may acquire the score of impressionword set as an item corresponding to each of the α1 axis, the α2 axis, aβ1 axis, a β2 axis, and a β3 axis in the impression word score list 114,and display the graph 128 using the score as the value of each axis.

In the search result display field 124A of the screen 121A, a bar graph129 indicating the value of each axis of the query is displayed.

In the present embodiment, as described above, the graph 126 indicatingthe relationship between the search result set data and the axis itemsand the graph 128 indicating the relationship between the query and theaxis items are displayed in the radar chart 125A. Therefore, accordingto the present embodiment, it allows the user to easily grasp the degreeof relationship between the search result set data and the query.

Second Embodiment

Hereinafter, the second embodiment will be described with reference tothe drawings. The second embodiment is different from the firstembodiment in that the item corresponding to the axis β is determinedbased on the query. In the following description of the secondembodiment, only the differences from the first embodiment will bedescribed. Those having the same functional configuration as those ofthe first embodiment are denoted by the same reference numerals as thoseused in the description of the first embodiment, and descriptionsthereof will be omitted.

FIG. 15 is a diagram describing functions of a search device of a secondembodiment. A search device 200A of the present embodiment includes asearch processing unit 240A.

The search processing unit 240A of the present embodiment includes theinput receiving unit 241, the analysis unit 242, the search unit 243,the impression word score output unit 244, an axis determination unit245A, the output unit 246, the index generation unit 247, and theimpression space learning unit 248.

When the axis determination unit 245A of the present embodiment acquiresthe impression word score 113 for each word by the impression word scoreoutput unit 244, the axis determination unit 245A acquires and storesthe impression word score list 114 indicating the maximum score of eachimpression word included in the impression word score 113 for each word.The axis determination unit 245A of the present embodiment refers to theimpression word score list 114, and determines the impression wordcorresponding to the axis α and axis β of the radar chart.

FIG. 16 is a flowchart that describes the processing of a searchprocessing unit of the second embodiment. Since the processing of stepS1601 of FIG. 16 is similar to the processing of step S901 of FIG. 9,description thereof is omitted.

The search processing unit 240A extracts the n number of impressionwords having a clear relationship with the query as an axis based on theinput query by the impression word score output unit 244, generates andstores the impression word score list 114 by the axis determination unit245A, and set them as the α axis (step S1602).

The axis determination unit 245A refers to the impression word scorelist 114 generated based on the query, extracts the m number of itemscorresponding to the β axis, and sets them as the β axis (step S1603).Details of the processing of step S1603 will be described later.

Since the processing of step S1604 and step S1605 of FIG. 16 is similarto the processing of step S904 and step S905 of FIG. 9, descriptionthereof is omitted.

Next, the processing of the axis determination unit 245A of the presentembodiment will be described with reference to FIG. 17. FIG. 17 is aflowchart describing processing of an axis determination unit of thesecond embodiment. The processing of FIG. 17 indicates the details ofthe processing of step S1603 of FIG. 16.

When the impression word score list 114 is generated in step S1602, theaxis determination unit 245A of the present embodiment extracts andstores each score of size from top n+1th to the mth score and theimpression word corresponding to corresponding to each score among thescores associated with each impression word from the impression wordscore list 114 (step S1701).

The axis determination unit 245A acquires the z number of search resultset data pieces in descending order of the degree of coincidence withthe search word group from the search result set data, and acquires thescore corresponding to the impression word stored in step S1701 withreference to the impression word score list 231 on the z number ofsearch result set data pieces (step S1702).

Hereinafter, the processing of step S1702 will be described. The axisdetermination unit 245A of the present embodiment acquires the z numberof search result set data pieces in descending order of the degree ofcoincidence with the search word group from the search result set dataacquired by the search of the search unit 243 in step S1601. The degreeof coincidence with the search word group may be calculated based on thenumber of search word groups included in the search result set data, theappearance frequency of each word included in the search word group inthe search result set data. As the degree of coincidence, the degree ofcoincidence in the case of performing information search may be usedgenerally.

When the axis determination unit 245A acquires the z number of searchresult set data pieces, the axis determination unit 245A refers to theimpression word score list 231 and acquires the score associated withthe impression word stored in step S1701 for each document ID of eachsearch result set data.

For example, it is assumed that impression words stored in step S1701are “healthy”, “quiet”, “fancy”, “lively”, “plentiful”, and “calm”. Itis assumed that the search result set data with document ID “2” isincluded in the z number of search result set data pieces.

In this case, the axis determination unit 245A refers to the impressionword score list 231, and acquires the score 9.53 of impression word“healthy” corresponding to the document ID “2”, the score 0.12 of theimpression word “quiet”, and the score 8.93 of the impression word“fancy” in step S1702 (see FIG. 8). Similarly, the axis determinationunit 245A acquires the corresponding score of impression words,“lively”, “plentiful”, and “calm”.

The axis determination unit 245A of the present embodiment acquires andstores the score for each impression word as described above for each ofthe z number of search result set data pieces.

The axis determination unit 245A calculates the dispersion of the scorefor each search result set data acquired in step S1702 for eachimpression word stored in step S1701 (step S1703).

The axis determination unit 245A sorts the impression words extracted instep S1701 by the dispersion value of the score, extracts the k numberof impression words in descending order of impression words with largedispersion values, and sets the extracted impression word as the β axes(step S1704). k is the number obtained by subtracting n from the numberof axes set in the radar chart, and is equal to the number of the βaxes.

As described above, the axis determination unit 245A of the presentembodiment extracts the impression word having variations in thestrength of the relationship with the search result set data among theimpression words with a small score indicating the strength of therelationship with the query compared with the α axis, and set as an itemcorresponding to the β axis.

In the present embodiment, by setting the item of the β axis in thisway, the value of each axis being close to each other, the radar chartsof search result set data pieces being similar to each other in theradar chart are suppressed. Therefore, according to the presentembodiment, it is possible to display the features of each search resultset data easy to understand when the search result is properlyevaluated, and the radar chart of each search result set data isdisplayed.

Third Embodiment

Hereinafter, the third embodiment will be described with reference tothe drawings. The third embodiment is different from the firstembodiment in that the dispersion obtained when acquiring the scoreindicating the strength of the relationship between the word and theimpression word is used when determining the axis. In the followingdescription of the third embodiment, only the differences from the firstembodiment will be described. Those having the same functionalconfiguration as those of the first embodiment are denoted by the samereference numerals as those used in the description of the firstembodiment, and descriptions thereof will be omitted.

First, the creation of the impression word map of the present embodimentto be described later will be described with reference to FIGS. 18A and18B.

The impression word map of the present embodiment is created, forexample, based on the result of a questionnaire or the like that isconducted on a large number of people to answer a score indicating thestrength of the relationship between words and impression words.

For example, in the present embodiment, the results of theabove-described questionnaire or the like are collected and the average,the maximum value, and the like of score indicating the strength of therelationship between the word and the impression word are stored in theimpression word maps as the score indicating the relationship betweenthe word the and impression word.

There are some words that have impression words that many people feelstrongly related to the word, and impression words that feeling of thestrength of the relationship to the word is different by people. In theformer impression word, it is presumed that the variation of the scoreindicating the strength of the relationship with the word becomes small.In the latter impression word, it is presumed that the variation of thescore indicating the strength of the relationship between the word andthe impression word becomes large. In the present embodiment, attentionis paid to the variation of the score of each impression word.

FIGS. 18A and 18B are graphs describing variation in scores in animpression word map of a third embodiment. FIG. 18A is a graph thatillustrates the variation of the score indicating the strength of therelationship between the word “natural” and the impression word“delicious”. FIG. 18B is a graph that illustrates the variation of thescore indicating the strength of the relationship between the word“natural” and the impression word “smell nice”.

In FIGS. 18A and 18B, the horizontal axis indicates the value of thescore answered in the questionnaire. In the examples of FIGS. 18A and18B, the score indicating the strength of the relationship between theword and the impression word is answered from 0 to 5. As the score islarger, it is assumed that the strength of the relationship that therespondents of the questionnaire feel between the word and theimpression word is stronger.

In FIGS. 18A and 18B, the vertical axis indicates the ratio of thenumber of responses for each score to the number of all respondents ofthe questionnaire.

In FIG. 18A, regarding the strength of the relationship between the word“natural” and the impression word “delicious”, the number of responseswith the score “4” is the largest, and the distribution of score isunimodal. For example, in FIG. 18A, it is found that the variation ofthe score is small. This indicates that many people feel that word“natural” and the impression word “delicious” has a strong relationship.

In FIG. 18B, regarding the strength of the relationship between the word“natural” and the impression word “smell nice”, there are many numbersof responses with the score “2” and score “4”, and the distribution ofscore is bimodal. For example, in FIG. 18B, compared with FIG. 18A, itis found that score variation is large. This indicates that the feelingof the strength of the relationship between word “natural” and theimpression word “smell nice” is different by people.

In the present embodiment, considering the variation in the feeling ofpeople, for example, the average of the scores acquired as the result ofquestionnaire responses is associated with the value indicating thescore variation with each combination of the word and the impressionword, and are stored in an impression word map.

In the examples of FIGS. 18A and 18B, with the combination of the word“natural” and the impression word “delicious”, the average of the scoreacquired as a response of the questionnaire is 3.8, and a standarddeviation indicating the variation is 0.68. Therefore, in the presentembodiment, the score “3.8” indicating the strength of the relationshipbetween the word “natural” and the impression word “delicious” and thestandard deviation “0.68” are associated with and stored in theimpression word map.

In the example of FIGS. 18A and 18B, with the combination of the word“natural” and the impression word “smell nice”, the average of the scoreacquired as a response to the questionnaire is 3.25, and the standarddeviation indicating the variation is 1.16. Therefore, in the presentembodiment, the score “3.25” and the standard deviation “1.16”indicating the strength of the relationship between the word “natural”and the impression word “smell nice” are associated with each other andstored in the impression word map.

In the present embodiment, in this way, by using the impression word mapwith which the score and the variation of the score are associated, forexample, it is possible to extract another impression word to replacethe axis when an impression word having a large standard deviation isused as an axis. For example, in the present embodiment, in a case wherean impression word that the feeling of the strength of the relationshipwith the word is different by people is extracted as an axis, it ispossible to extract another impression word to replace the impressionword, and present to the user.

Therefore, according to the present embodiment, when displaying thesearch result set data acquired by the search processing by the searchword group based on the query, it is possible to display the impressionword that reflects the intention of the user as a candidate of an item(axis).

Hereinafter, a search device of the present embodiment will bedescribed. FIG. 19 is a diagram describing functions of a search deviceof the third embodiment. A search device 200B of the present embodimentincludes the search target index storage unit 210, an impression wordmap storage unit 220A, a search target score storage unit 230A, and asearch processing unit 240B.

An impression word map 221A is stored in the impression word map storageunit 220A. In the impression word map 221A, a score indicating therelationship between the impression word and the word and the standarddeviation acquired when the score is acquired are associated with eachother for each impression word. Details of the impression word map 221Awill be described later.

An impression word score list 231A is stored in the search target scorestorage unit 230A. The impression word score list 231A is created by theprocessing of the search processing unit 240B. Details of the impressionword score list 231A will be described later.

The search processing unit 240B of the present embodiment includes theinput receiving unit 241, the analysis unit 242, the search unit 243, animpression word score output unit 244A, the axis determination unit245A, the output unit 246, the index generation unit 247, an impressionspace learning unit 248A, and a candidate notification unit 249.

The impression word score output unit 244A of the present embodimentoutputs the score indicating the strength of the relationship betweenthe word represented by a vector and the impression word and thestandard deviation of the score based on the impression word map 221Aand a conversion model 250A output by the impression space learning unit248A described later.

The axis determination unit 245A of the present embodiment outputs animpression word as an axis of a radar chart for displaying the searchresult and an impression word that is replaceable with the impressionword based on the score and the standard deviation for each impressionword output by the impression word score output unit 244A. Theimpression word replaceable with the impression word determined as anaxis is referred to as a candidate axis. The impression word as acandidate axis of the present embodiment is, for example, a thirdfeature word group that is a candidate of the feature word group (firstfeature word group) indicating the feature of the content estimated fromthe query.

The impression space learning unit 248A of the present embodimentoutputs the conversion model 250A based on the document data of thesearch target stored in the search target document database 400 and theimpression word map 221A. The conversion model 250A of the presentembodiment is a model that inputs a semantic vector of a word acquiredby distributed representation, and outputs the score for an impressionword associated with the word and the standard deviation of the scores.

The candidate notification unit 249 of the present embodiment notifiesthe impression word as the candidate axis output by the axisdetermination unit 245A.

Hereinafter, the impression word map 221A of the present embodiment willbe described with reference to FIG. 20. FIG. 20 is a table illustratingan example of the impression word map of the third embodiment.

The impression word map 221A of the present embodiment is created inadvance and given to the search device 200B. In the impression word map221A of the present embodiment, the score (value) indicating thestrength of the relationship with an impression word prepared in advanceand the standard deviation indicating the variation of the score areassociated with each other for each word.

In FIG. 20, for example, the score indicating the strength of therelationship between the word “restaurant” and the impression word“lively” is 1.6, and the standard deviation σ thereof is 0.5. The scoreindicating the strength of the relationship between the word“restaurant” and the impression word “stuffed” is 3.1, and the standarddeviation σ thereof is 1.7. Therefore, between the impression word“lively” and the impression word “stuffed”, many people feel that theimpression word “lively” has a higher strength of the relationship thanthat of the word “restaurant”.

In the present embodiment, a value indicating the variation of the scoreindicating the strength of the relationship between the word and theimpression word as the standard deviation, but not limited thereto. Thevalue indicating the variation of the score indicating the strength ofthe relationship between the word and the impression word may be held asdispersion.

Next, the impression word score list 231A of the present embodiment willbe described with reference to FIG. 21.

FIG. 21 is an example table of an impression word score list of thethird embodiment. In FIG. 21, an example of the impression word scorelist 231A acquired from the document data related to restaurants and thelike in the search target document database 400 is illustrated. Theimpression word score list 231A may be created in advance and given tothe search device 200B.

In the impression word score list 231A, a document ID that specifies thedocument data, a name of the store indicated by the document data, textdata indicating the content of the document data, and a score indicatingthe strength of the relationship with each impression word included inthe impression word score list 231A are associated with one another. Theimpression words included in the impression word score list 231A are thesame as the impression word included in the impression word map 221A.

Next, the processing of the search processing unit 240B of the presentembodiment will be described with reference to FIG. 22. FIG. 22 is aflowchart describing processing of a search processing unit of the thirdembodiment.

The search processing unit 240B of the present embodiment acquires thesearch word group by morphological analysis or the like from the queryby the analysis unit 242, searches the index information 211 with thesearch word group by the search unit 243, and specifies the document IDcorresponding to the search word group (step S2201).

The search processing unit 240B extracts the n number of impressionwords that are assumed to have a strong relationship with the query asaxes and set the impression words as a axes based on the query input bythe impression word score output unit 244A, and extract the impressionword as a candidate axis (step S2202). Details of the processing of stepS2202 will be described later.

Since the processing from step S2203 to step S2205 of FIG. 22 is similarto the processing from step S903 to step S905 of FIG. 9, descriptionthereof is omitted.

When the output unit 246 draws a radar chart in step S2205, the searchprocessing unit 240B determines whether or not the impression word as acandidate axis is extracted by the candidate notification unit 249 instep S2202 (step S2206). In step S2206, in a case where the impressionword is not extracted, the search processing unit 240B ends theprocessing.

In step S2206, in a case where the impression word is extracted, thecandidate notification unit 249 outputs the impression word extracted asa candidate axis in step S2202 on a screen that a radar chart isdisplayed, notifies that it is possible to replace the axis of radarchart with a candidate axis (step S2207), and ends the processing.

Next, details on the processing of step S2202 of FIG. 22 will bedescribed with reference to FIG. 23. FIG. 23 is a flowchart describingprocessing of an impression word score output unit and an axisdetermination unit of the third embodiment.

The impression word score output unit 244A of the present embodimentconverts each word included in the search word group acquired in stepS2201 to the distributed representation (step S2301).

The impression word score output unit 244A refers to the conversionmodel 250A, acquires the score that indicates the strength of therelationship between each word and each impression word and the standarddeviation for each word (step S2302), and proceeds to step S2303. Forexample, the impression word score output unit 244 inputs the semanticvector for each word acquired in step S2301 with respect to theconversion model 250A, and acquires the score of each impression wordand the standard deviation for each word output from the conversionmodel 250A.

Since the processing of step S2303 and step S2304 are similar to theprocessing of step S1003 and step S1004 of FIG. 10, the descriptionthereof is omitted.

Following step S2304, the search processing unit 240B determines whetheror not there is an impression word that has the standard deviation valuelarger than a threshold among the standard deviations of the extractedimpression words in step S2304 by the axis determination unit 245A (stepS2305).

In step S2305, in a case where there is no corresponding impressionword, the search processing unit 240B ends the processing and proceedsto step S2203.

In step S2305, in a case where there is a corresponding impression word,the axis determination unit 245A specifies the impression word that hasthe standard deviation larger than the threshold from the extractedimpression words (step S2306).

The axis determination unit 245A extracts the m number of impressionwords that has the maximum score of n+1th rank or lower from top, andthe maximum score is larger than the value obtained by subtracting thestandard deviation from the maximum score of the impression wordspecified in step S2306 (step S2307).

The axis determination unit 245A stores the m number of extractedimpression words as candidate axes replaceable with the impression wordspecified in step S2306 (step S2308), and proceeds to step S2203.

Hereinafter, the extraction of a candidate axis by the search processingunit 240B of the present embodiment will be described in details withreference to FIG. 24. FIG. 24 is a diagram describing extraction of acandidate axis by the search processing unit of the third embodiment.

In FIG. 24, a case that a query “restaurant using natural materials andvegetables” is input, and “natural”, “material”, “vegetable”,“restaurant” are extracted by the analysis unit 242 as a search wordgroup 111A is illustrated.

The search processing unit 240B acquires the semantic vector of eachword included in the search word group 111A and input as the conversionmodel 250A by the impression word score output unit 244A, and acquiresthe score and the standard deviation for each impression word includedin the impression word map 221A for each word.

In FIG. 24, information in which each word included in the search wordgroup 111A is associated with the score and the standard deviation foreach impression word is referred to as an impression word score 113A foreach word.

Next, the axis determination unit 245A acquires the maximum score foreach impression word from the impression word score 113A for each word.

In the impression word score 113A for each word of FIG. 24, the scoreindicating the strength of the relationship between the impression word“delicious” and the word “restaurant” and the standard deviation are 3.4and 0.2, and the score indicating the strength of the relationshipbetween the impression word “delicious” and the word “vegetable” and thestandard deviation are 3.7 and 0.3. The score indicating the strength ofthe relationship between the impression word “delicious” and the word“natural” and the standard deviation are 3.8 and 0.7, and the scoreindicating the strength of the relationship between the impression word“delicious” and the word “material” and standard are 3.2 and 0.5.

Therefore, the axis determination unit 245A acquires “3.8” as themaximum value of the score of the impression word “delicious” andacquires “0.7” as the standard deviation of the impression word“delicious”.

In the impression word score 113A for each word, the score indicatingthe strength of the relationship between the impression word “healthy”and the word “restaurant” and the standard deviation are 3.0 and 0.5,and the score indicating the strength of the relationship between theimpression word “healthy” and the word “vegetable” and the standarddeviation are 4.2 and 0.3. The score indicating the strength of therelationship between the impression word “healthy” and the word“natural” and the standard deviation are 3.1 and 1.2, and the scoreindicating the strength of the relationship between the impression word“healthy” and the word “material” and the standard deviation are 3.0 and0.7.

Therefore, the axis determination unit 245A acquires “4.2” as themaximum score of the impression word “healthy” and “0.3” as the standarddeviation of the impression word “healthy”.

The axis determination unit 245A of the present embodiment stores themaximum value of the score and the standard deviation for eachimpression word acquired in this manner as an impression word score list114A.

The axis determination unit 245A extracts the n number of impressionwords in descending order of the impression words having the maximumscore in the impression word score list 114A, and set them as the aaxis. In the example of FIG. 24, n is 3.

In the impression word score list 114A, the impression word having themaximum score is “healthy”, the impression word having the next maximumscore is “delicious”, and the impression word having the next maximumscore is “smell nice”.

Therefore, the axis determination unit 245A of the present embodimentdetermines the impression word “healthy”, the impression word“delicious”, and the impression word “smell nice” as the α axis. Forexample, the axis determination unit 245A uses the impression word“healthy”, the impression word “delicious”, and the impression word“smell nice” as an item when displaying the search result set data.

The axis determination unit 245A of the present embodiment refers to theimpression word score list 114A, and determines whether or not thestandard deviation of each of the impression word “healthy”, theimpression word “delicious”, and the impression word “smell nice”determined as the α axis is larger than a predetermined threshold.

The predetermined threshold of the standard deviation will be describedas 1.0. Any value may be set as the predetermined threshold.

In the impression word score list 114A, the standard deviation of theimpression word “healthy” is 0.3, the standard deviation of theimpression word “delicious” is 0.7, and both values are equal to or lessthan the predetermined threshold.

On the other hand, the standard deviation of the impression word “smellnice” is 1.2, which is larger than the predetermined threshold. Thisindicates that the feeling of the strength of the relationship betweenthe impression word “smell nice” and the word included in the searchword group 111A is different depending on the people.

In the present embodiment, with reference to the impression word scorelist 114A, the m number of impression words that have the score of n+1thrank or lower from top, and the score is the value obtained bysubtracting the standard deviation 1.2 from the score 3.2 of theimpression word “smell nice” is equal to or higher than 2.0 areextracted in descending order of the score as a candidate axis.

In the example of FIG. 24, m is 2. Therefore, the axis determinationunit 245A extracts two impression words that have a score 4th ranked orlower from the top in the impression word score list 114A in descendingorder of the score, and 2.0 or higher.

In FIG. 24, the impression word “stuffed” with the score of 3.1 and theimpression word “stomach-friendly” with the score of 2.8 are extractedas a candidate axis.

The output unit 246 of the search processing unit 240B displays theimpression word “healthy”, the impression word “delicious”, and theimpression word “smell nice” as an item (axis) 116 that indicates thesearch result set data on a screen 115 that displays the search resultset data. The output unit 246 displays a bar graph 117 indicating thevalue of each item (axis) on the screen 115. In the example of FIG. 24,an example in which “Kyoto cuisine BB” is extracted as the search resultset data is displayed.

At this time, the candidate notification unit 249 displays anotification field 118 indicating that, in association with theimpression word “smell nice”, the impression word is changeable with theimpression word “stuffed” or the impression word “stomach-friendly”extracted as a candidate axis.

In the present embodiment, by displaying the notification field 118 asdescribed above, for example, in a case where the user feels that therelationship between the search word group 111A and the impression word“smell nice” displayed as an item is weak, the user may change the itemto another impression word. In the present embodiment, by displaying thenotification field 118, it is possible to inform the user who thinksthat the relationship between the search word group 111A and theimpression word “smell nice” is weak, that it is possible to reflect theintention (feeling) of the user.

Hereinafter, a display example of the present embodiment will bedescribed with reference to FIGS. 25 and 26. FIG. 25 is a first diagramillustrating a display example of the third embodiment.

On a screen 115A illustrated in FIG. 25, a bar graph 119 indicating thevalue of each axis of the query is displayed in addition to the item(axis) 116 indicating the search result set data, the bar graph 117indicating the value of each axis (item), and the notification field 118of a candidate axis replaceable with the impression word “smell nice”.

FIG. 26 is a second diagram illustrating a display example of the thirdembodiment.

In a screen 115B illustrated in FIG. 26, the search result set data isdisplayed by a radar chart 130.

In the radar chart 130, a graph 117A and a graph 119A are displayed. Thegraph 117A indicates the relationship between the search result set dataand the item as an axis, and the graph 119A indicates the relationshipbetween the query and the item as an axis.

In the radar chart 130, an α1 axis is “healthy”, an α2 axis is“delicious”, and an α3 axis is “smell nice”, and the impression words“stuffed” and “stomach-friendly” extracted as candidate axis are set asa β1 axis and a β2 axis respectively.

As described above, in the present embodiment, when displaying the radarchart, the impression word extracted as a candidate axis may be the βaxis. In the present embodiment, when displaying the radar chart, the βaxis is determined based on the same method in the first and secondembodiment, and another candidate axis associated with the impressionword having the standard deviation larger than the predeterminedthreshold among the impression words as the α axis may be notified.

In the screen 115B of FIG. 26, for example, the β axis of the radarchart 130 is, for example, the word extracted from the search word group111A extracted from the query, and the notification field 118 isdisplayed in association with the α3 axis.

As described above, in the present embodiment, in a case where there isa certain variation or more in the score of the impression word as anaxis, it is possible to display the search result in which the intentionof the user is reflected by notifying the user about another impressionword may be used as an axis.

Fourth Embodiment

Hereinafter, the fourth embodiment will be described with reference tothe drawings. The fourth embodiment is different from the thirdembodiment in that the axes are changed after receiving the selection ofthe impression word displayed as a candidate axis. In the description ofthe fourth embodiment below, only the differences from the thirdembodiment will be described. Those having the same functionalconfiguration as those of the third embodiment are denoted by the samereference numerals as those used in the description of the thirdembodiment, and descriptions thereof will be omitted.

FIG. 27 is a diagram describing functions of a search device of a fourthembodiment. A search device 200C of the present embodiment includes asearch processing unit 240C.

The search processing unit 240C of the present embodiment includes anaxis change unit 251 in addition to each unit of the search processingunit 240B.

When the selection of the candidate axis displayed in association withthe axis is received in the terminal device 300 and the like, forexample, the axis change unit 251 of the present embodiment displays thegraph in which the axis is changed according to the selection withrespect to the output unit 246. In a case where an axis is specified bythe user, the axis change unit 251 of the present embodiment receivesthe selection of the candidate axis, and changes the impression wordhaving the minimum value among the impression words with axis other thanthe specified axes with the impression word as the selected axis.

Hereinafter, the processing of the axis change unit 251 of the searchprocessing unit 240C of the present embodiment will be described withreference to FIG. 28.

FIG. 28 is a flowchart describing axis change processing in the fourthembodiment. In the search processing unit 240C of the presentembodiment, the axis change unit 251 determines whether or not toreceive the selection of the candidate axis in the notification of thecandidate axis (step S2801). In step S2801, in a case where theselection is not received, the axis change unit 251 waits until theselection is received.

In step S2801, in a case where the selection of the axis is received,the axis change unit 251 determines whether or not there is the axisspecified by the user (step S2802). “specified” is the specification formaintaining the display. Therefore, in the present embodiment, thedisplay of the specified axis is maintained.

In step S2802, in a case where there is a specified axis, the axischange unit 251 changes the axis with the smallest maximum score to theselected candidate axis among the axes (impression word) that are notspecified by the user (step S2803).

In step S2802, in a case where there is no specified axis, the axischange unit 251 changes the axis with the lowest score to the selectedcandidate axis among the displayed axes of the graph (step S2804).

Following steps S2803 and S2804, the axis change unit 251 instructs thedisplay of the graph in which the axis is changed to the output unit 246(step S2805), and ends the processing.

Hereinafter, the change of the axis will be described in details withreference to FIGS. 29A to 29C. FIGS. 29A to 29C are diagrams describingthe change of axis.

In FIG. 29A, a screen 291 in which a specification field 292 to specifythe impression word extracted as the α axis is displayed in the screen115 that the displays search result set data.

A screen 291A illustrated in FIG. 29B is an example screen transitioningfrom the screen 291 in a case where the specification of an axis in thespecification field 292 is not performed, and the candidate axis“stuffed” displayed in the notification field 118 is selected.

In the screen 291, the impression words extracted as the axes 116 are“healthy”, “delicious”, and “smell nice”. In the notification field 118,when “stuffed” is selected, since there is no specification of thespecification field 292, the axis change unit 251 changes the impressionword having the smallest maximum score to the impression word “stuffed”among the impression words “healthy”, “delicious”, “smell nice” (seeFIG. 24).

Among the axes 116, the impression word having the smallest maximumscore is “smell nice”. Therefore, in the screen 291A, axes 116A in whichthe impression word “smell nice” is changed to the impression word“stuffed” is displayed. In the screen 291A, a bar graph 117A matchedwith the axes 116A is displayed.

In the present embodiment, the changed impression word “smell nice” andthe impression word “stomach-friendly” set as a candidate axis in thenotification field 118 are displayed in a notification field 118A as acandidate axis replaceable with the impression word “stuffed” in thescreen 291A.

In the present embodiment, as described above, since the changed axis isnotified as a candidate axis, in a case of displaying the graph beforethe change, the changed axis “smell nice” displayed in the notificationfield 118A may be selected and it is possible to easily return to thedisplay before changing the axis.

A screen 291B illustrated in FIG. 29C is an example screen transitioningfrom the screen 291 in a case where the impression word “stuffed” isselected as a candidate axis in the specification field 292 of thescreen 291 in a state that the impression word “smell nice” as an axisis specified.

In a specification field 292A of the screen 291B, a check mark thatspecifies the impression word “smell nice” is displayed. In the screen291B, the display of the impression word “smell nice” as an axis ismaintained.

Among the axes 116, the impression word having the smallest maximumscore and the impression word other than the impression word “smellnice” is “delicious”. Therefore, on the screen 291B, axes 116B in whichthe impression word “delicious” is changed to the impression word“stuffed” are displayed. In the screen 291B, a bar graph 117B matched tothe axes 116A is displayed.

In the screen 291B, the changed impression word “delicious” and theimpression word “stomach-friendly” specified as the candidate axis inthe notification field 118 are displayed in a notification field 118B asa candidate axis replaceable with the impression word “stuffed”.

As described above, according to the present embodiment, in the case ofchanging axis, it is possible to maintain the axis specified by theuser.

In FIGS. 29A to 29C, an example of a bar graph is described, but ispossible to maintain the axis similarly specified as the change axis onthe screen in which a radar chart is displayed.

Fifth Embodiment

Hereinafter, the fifth embodiment will be described with reference tothe drawings. The fifth embodiment is different from the fourthembodiment in that in a case where the axis is changed, the score of theimpression word map is corrected in response to the change. In thedescription of the fifth embodiment below, only the differences from thefourth embodiment will be described. Those having the same functionalconfiguration as those of the fourth embodiment are denoted by the samereference numerals as those used in the description of the fourthembodiment, and descriptions thereof will be omitted.

FIG. 30 is a diagram describing functions of a search device of a fifthembodiment. A search device 200D of the present embodiment includes thesearch target index storage unit 210, an impression word map storageunit 220B, the search target score storage unit 230A, a searchprocessing unit 240D, and a user information storage unit 270.

The impression word map storage unit 220B of the present embodimentstores the impression word map 221A and a correction list 222. Theimpression word score output unit 244A refers to the correction list 222when correcting the acquired score.

The search processing unit 240D of the present embodiment includes acorrection list generation unit 252 and a score correction unit 253 inaddition to the search processing unit 240C of the fourth embodiment.

In a case where the axis is changed by the axis change unit 251, thecorrection list generation unit 252 updates the correction list 222 tocorrect the score output from the impression word score output unit 244Aaccording to the change of axis or the maintenance of the display.Details of the correction list 222 will be described later.

The score correction unit 253 corrects the score output from theimpression word score output unit 244A based on the correction list 222.

The user information storage unit 270 of the present embodiment stores auser information 271. The user information 271 is information foridentification of the user using a search system including the searchdevice 200D. For example, the user information 271 is a user ID foridentifying the user, a password, and the like. The user information 271of the present embodiment may be given to the search device 200D inadvance.

Next, the processing of the correction list generation unit 252 of thepresent embodiment will be described with reference to FIG. 31. FIG. 31is a flowchart describing processing of a correction list generationunit of the fifth embodiment.

Since the processing from step S3101 to step S3105 of FIG. 31 is similarto the processing from step S2801 to step S2805 of FIG. 28, thedescription thereof is omitted.

When the candidate axis is changed, the search processing unit 240Dupdates the correction list 222 according to the user information of theuser who made the change and the change of axis or the specification ofthe axis by the correction list generation unit 252 (step S3106), andends the processing.

Hereinafter, the processing of step S3106 will be further explained.First, the case in which the specification of the axis by the user isnot performed when changing the axis will be described.

In this case, the correction list generation unit 252 stores theimpression word indicating the axis to be changed, the value to besubtracted from the score of the impression word, the search word groupacquired from the query, and the user information in association witheach other in the correction list 222. The impression word indicatingthe axis to be changed is the impression word having the smallestmaximum score among the impression words extracted by the impressionword score output unit 244A as the axis.

Next, a case where the specification of the axis is performed by theuser when changing the axis will be described. In this case, thecorrection list generation unit 252 stores the impression wordindicating the axis specified by the user, the value added to the scoreof the impression word, the search word group acquired from the query,and the user information in association with each other in thecorrection list 222.

Hereinafter, the correction list 222 of the present embodiment will bedescribed with reference to FIG. 32. FIG. 32 is a diagram illustratingan example of a correction list of the fifth embodiment.

The correction list 222 of the present embodiment includes the user ID,the search word group, and the change point as an information item inassociation with each other. The value of the item “user ID” indicatesthe user ID that identifies the user. The value of the item “search wordgroup” indicates the search word group acquired from the input query.The value of the item “change point” indicates the changed impressionword and the value to be subtracted from the score of the impressionword, or the impression word specified by the user as an axis thatmaintains the display and the value to be added to the score of theimpression word.

In the following description, in the correction list 222, informationincluding the value of the item “user ID” and the other values isreferred to as correction information. In the description below, thevalue (first correction value) to be added to the score of theimpression word or the value (second correction value) to be subtractedfrom the score of the impression word is referred to as a scorecorrection value. The score correction value may be set in advance andstored in the correction list generation unit 252.

In the example of FIG. 32, in correction information 222-1 includinguser ID “0001”, the values of the item “search word group” are“natural”, “material”, “vegetable”, and “restaurant”, and the value ofthe item “change point” is “smell nice” and “−0.1”.

When the user of the user ID “0001” displays the search result set datasearched with the search word group “natural”, “material”, “vegetable”,and “restaurant” acquired from the input query, the correctioninformation 222-1 indicates that impression word “smell nice” set as anaxis is changed to another impression word. At this time, the correctioninformation 222-1 indicates that there is no impression word set by theuser to maintain the display in the axes, and the score correction valueof “0.1” is subtracted from the impression word “smell nice”.

In correction information 222-2 of FIG. 32, when the user of the user ID“0002” displays the search result set data searched with the search wordgroup “plentiful” and “Chinese” acquired from the input query, it isindicated that the display of the impression word “stuffed” ismaintained among the axes by the user. The correction information 222-2indicates that the score correction value of “0.1” is added to the scoreof the impression word “stuffed”.

Next, the processing of the impression word score output unit 244A, theaxis determination unit 245A, and the score correction unit 253 of thepresent embodiment will be described with reference to FIG. 33. FIG. 33is a flowchart describing processing of an impression word score outputunit, an axis determination unit, and a score correction unit of thefifth embodiment.

In the search device 200D of the present embodiment, the input of theuser information is received and the login processing is performedbefore the user inputs the query.

Since the processing from step S3301 to step S3302 of FIG. 33 is similarto the processing from step S2301 to step S2302 of FIG. 23, thedescription thereof is omitted.

In step S3302, when the score and the standard deviation for eachimpression word are acquired, the score correction unit 253 determineswhether or not there is the correction information including the inputuser ID in the correction list 222 (step S3303). In step S3303, in acase where there is no corresponding correction information, the searchprocessing unit 240D proceeds to step S3306 described later.

In step S3303, in a case where there is corresponding correctioninformation, the score correction unit 253 determines whether or notthere is search word group acquired from the input query and correctioninformation that matches with the search word group in the correspondingcorrection information (step S3307). In step S3307, in a case wherethere is no corresponding correction information, the search processingunit 240D proceeds to step S3306 described later.

In step S3307, in a case where there is corresponding correctioninformation, the score correction unit 253 refers to the value of theitem “change point” of the correction information, corrects the scorefor each impression word acquired in step S3302 (step S3305), andproceeds to step S3306.

Since the processing from step S3306 to step S3311 is similar to theprocessing from step S2303 to step S2308 of FIG. 23, the descriptionthereof is omitted.

Hereinafter, the processing of FIG. 33 will be described in details withreference to FIG. 24. For example, a case in which the user of the userID “0001” inputs a query of “restaurant using natural materials andvegetables” and the search word group 111A is acquired will bedescribed.

In this case, the search processing unit 240D acquires the impressionword score 113A for each word by the impression word score output unit244A. At this time, the score correction unit 253 refers to thecorrection list 222, and determines whether or not there is correctioninformation including the user ID “0001”.

In the correction list 222, there is the correction information 222-1that includes user ID “0001” (see FIG. 32). Therefore, the scorecorrection unit 253 determines whether or not the value of the item“search word group” of the correction information 222-1 and the searchword group 111A acquired from the input query matches.

Since they match, the score correction unit 253 refers to item “changepoint” of the correction information 222-1, and performs a correction ofsubtracting 0.1 from the score corresponding to the impression word“smell nice” of the impression word score 113A for each word.

As described above, in the present embodiment, in a case where the userchanges the impression word as the axis once, or specified theimpression word to maintain the display, the operation history is storedfor each user as the correction information. In the present embodiment,in a case where the search word group acquired from the query matches,it is possible to reflect the intention of the user on the item whendisplaying the search result set data by correcting the score of theimpression word using the correction information.

Sixth Embodiment

Hereinafter, the sixth embodiment will be described with reference tothe drawings. The sixth embodiment is different from the firstembodiment in that the appropriate number of search result set datapieces is displayed when the operation to change the score of theimpression word is received on the output axis. In the followingdescription of the sixth embodiment, only the differences from the firstembodiment will be described. Those having the same functionalconfiguration as those of the first embodiment are denoted by the samereference numerals as those used in the description of the firstembodiment, and descriptions thereof will be omitted.

FIG. 34 is a diagram describing a display example of the sixthembodiment. On a screen 341 illustrated in FIG. 34, a plurality ofoutput axes 342 and search result set data pieces 343 are displayed.

In the screen 341, the output axes 342 indicates the impression wordwith a strong relationship with the search word group based on the inputquery.

On the screen 341, a scale 344 and a slider 345 to change the score ofthe impression word for each impression word as the output axes 342 aredisplayed. In the present embodiment, when the slider 345 is moved onthe scale 344 and the slider 345 stops on a graduation on the scale 344set in advance, the score of the impression word is changed to the valuecorresponding to the position where the slider 345 is stopped. When thescore of the impression word is changed, the search result set data 343becomes the search result using the score after the change.

In the description below, the slider on the scale stops on thegraduation, and the graduation marked on the scale is referred to as aslider stop position. In the description below, the value (graduation)indicated by the slider stop position on the scale may be represented asa slider value.

The search result set data 343 is document data indicated by thedocument ID specified by the search processing. In the screen 341, it isdisplayed as the score of each output axis “premier”, “calm”, and“plentiful” and other document data extracted as search result set datapieces in the document data with highest degree of match with the query.

The display of the search result set data in a case where the score ofthe impression word is changed will be described. The number of searchresults (search result set data pieces) corresponding to the score ofthe impression word is different depending on the distribution of thescore in the plurality of extracted search result set data. In a casewhere the value of the score is changed by moving the slider on thescale, a large number of search result set data pieces may be extracted,or only a small number of search result set data pieces may beextracted.

FIGS. 35A and 35B are diagrams describing change in score of impressionwords. FIG. 35A is a graph indicating the distribution of each score ofthe impression words “calm” and “plentiful” as an output axis. FIG. 35Bis a diagram illustrating the relationship between the score indicatedby the slider and the search result set data.

As illustrated in FIG. 35A, the impression word “calm” has a smalldeviation of score with respect to the search result set data, and theimpression word “plentiful” has a large deviation of score with respectto the search result set data.

As illustrated in FIG. 35B, for example, a case where five slider stoppositions (slider value “1” to “5”) are at equal interval are providedbetween the slider stop position (slider value “0”) as a start of thescale and the slider stop position (slider value “6”) at the end pointof the scale, and the score is changed by moving the slider to eachslider stop position is considered.

In this case, since the impression word “calm” has a small deviation ofscore, regardless of the slider stop position of the slider, a certainnumber of search result set data pieces is output.

On the other hand, since the impression word “plentiful” has a largedeviation of score, depending on the slider stop position, the searchresult set data may not be output, or a lot of search result set datamay be output. For example, in a case where the score of the impressionword “plentiful” is changed by moving the slider on the scale, thesearch result set data may not be output properly.

In the present embodiment, the distribution of the impression word asthe output axis is calculated, and the slider stop position on the scaleis associated with the variation range of the score according to thescore distribution. For example, according to the present embodiment,each slider stop position on the scale and the variation range of thescore in association with each other is displayed so that the number ofsearch result set data pieces displayed corresponding to the variationrange of the score to be a predetermined number for each output axis.The predetermined number may be a fixed number or a number within apredetermined range. For example, the number of search result set datapieces displayed corresponding to the variation range of the score maybe 10, or 8 to 12.

In the present embodiment, by performing the association, a certainnumber of search result set data pieces may be displayed when the slideris moved on the scale.

According to the present embodiment, the score of the output axes(impression words) is changed, and the search result set datacorresponding to the changed score is presented to the user. Therefore,according to the present embodiment, for example, in a case whereinformation desired by the user is not acquired from the score estimatedfrom the query, new search result in which the score of the impressionword is changed may be provided to the user.

Hereinafter, the functions of a search device 200E of the presentembodiment will be described with reference to FIG. 36. FIG. 36 is adiagram describing functions of a search device of the sixth embodiment.

The search device 200E of the present embodiment includes the searchtarget index storage unit 210, the impression word map storage unit 220,the search target score storage unit 230A, and a search processing unit240E.

The search processing unit 240E of the present embodiment includes theinput receiving unit 241, the analysis unit 242, the search unit 243,the impression word score output unit 244, the axis determination unit245, an output unit 246A, the index generation unit 247, the impressionspace learning unit 248, a score distribution calculation unit 260, anda scale calculation unit 261.

The score distribution calculation unit 260 of the present embodimentcalculates the score distribution for each impression word determined asthe output axis by the axis determination unit 245. For example, thescore distribution calculation unit 260 generates a histogram indicatingthe score distribution in the impression word score list 231A for eachimpression word determined as the output axis by the axis determinationunit 245.

The scale calculation unit 261 of the present embodiment calculates thevariation range of the score corresponding to the slider stop positionattached on the scale indicating the score of the impression word basedon the distribution of the score calculated by the score distributioncalculation unit 260. For example, the scale calculation unit 261creates and stores a correspondence table 410 in which the slider valueis associated with the variation range of the score.

Details of the processing of the score distribution calculation unit 260and the scale calculation unit 261 and the correspondence table 410 willbe described later.

The output unit 246A of the present embodiment refers to thecorrespondence table 410, and displays a radar chart of the searchresult set data.

Hereinafter, the processing of the search processing unit 240E of thepresent embodiment will be described with reference to FIG. 37. FIG. 37is a first flowchart describing processing of a search processing unitof the sixth embodiment.

Since the processing from step S3701 to step S3704 of FIG. 37 is similarto the processing from step S901 to step S904 of FIG. 9, the descriptionthereof is omitted.

In step S3704, when the n number of impression words that has a clearrelationship with the query are extracted and set as a axes by the axisdetermination unit 245, the search processing unit 240E calculates thescore distribution for each output axis (a axis) in search result setdata pieces specified in step S3701 by the score distributioncalculation unit 260 (step S3705).

The search processing unit 240E associates the slider stop position withthe variation range of the score for each output axis by the scalecalculation unit 261 (step S3706).

The search processing unit 240E sets the score of the impression wordwhen the α axis is set as an initial value on the scale by the outputunit 246A (step S3707).

The output unit 246A draws the search result set data with the sliderand the scale that correspond with the impression word as the outputaxis (step S3708), and ends the processing.

Next, the processing of the score distribution calculation unit 260 ofthe present embodiment will be described with reference to FIG. 38. FIG.38 is a second flowchart describing processing of the search processingunit of the sixth embodiment. In FIG. 38, the details of the processingof step S3705 of FIG. 37 is illustrated.

The score distribution calculation unit 260 of the present embodimentselects one axis among the output axes determined by the axisdetermination unit 245, and fixes the score of the other output axes(step S3601). The score distribution calculation unit 260 changes thescore of the selected output axis (step S3602). The score of theselected output axis may be increased or decreased for eachpredetermined interval. For example, in a case where the minimum valueof the score of the output axis is 0, and the maximum value is 10, thescore is changed to 0, 1, 2, . . . , 10, and so on.

The score distribution calculation unit 260 performs vector matchingbetween the score of each output axis and the score of each impressionword corresponding to the document data extracted as the search resultset data in a state that the score of the selected output axis ischanged (step S3603). For example, the score distribution calculationunit 260 may perform vector matching based on cosine similarity. It isassumed that a plurality of document data are extracted as the searchresult set data.

The score distribution calculation unit 260 specifies the document ID inwhich the result of the vector matching is equal to or greater than thepredetermined threshold, and acquires the number of specified documentIDs (step S3604).

The score distribution calculation unit 260 determines whether or notthe score of the selected output axis is changed from the minimum valueto the maximum value (step S3605). For example, the score distributioncalculation unit 260 determines whether or not the vector matching isperformed for each predetermined interval from the minimum value to themaximum score.

In step S3605, in a case where the vector matching is not performed foreach predetermined interval from the minimum score to the maximum score,the score distribution calculation unit 260 returns to step S3602.

In step S3605, in a case where the vector matching is performed for eachpredetermined interval from the minimum score to the maximum score, thescore distribution calculation unit 260 generates a histogram from thenumber of document IDs of each predetermined interval (step S3606).

The score distribution calculation unit 260 determines whether or notthe processing from step S3601 to step S3606 is performed for everyoutput axes (step S3607). The score distribution calculation unit 260may determine whether or not the processing from step S3601 to stepS3606 is performed for the α axis among the output axes.

In step S3607, in a case where processing is not performed for alloutput axes, the score distribution calculation unit 260 returns to stepS3601. In step S3607, when the processing is performed for all outputaxes, the score distribution calculation unit 260 ends the processing.

Next, the processing of the scale calculation unit 261 of the presentembodiment will be described with reference to FIG. 39. FIG. 39 is athird flowchart describing processing of the search processing unit ofthe sixth embodiment. In FIG. 38, details of the processing of stepS3706 in FIG. 37 is illustrated.

The scale calculation unit 261 of the present embodiment acquires ascore range (from minimum score to maximum score), a histogram, and theX number of document IDs acquired when generating the histogram for eachoutput axis (step S3901).

The scale calculation unit 261 acquires the N number of slider values(graduation) on the scale for each output axis (step S3902).

The scale calculation unit 261 selects an output axis (step S3903), andcreates a correspondence table so that the number of document IDscorresponding the number of slider values is set to be X/N (step S3904).

The scale calculation unit 261 determines whether or not thecorrespondence table is created for all output axes (step S3905). Instep S3905, in a case where the correspondence table is not created forall output axes, the scale calculation unit 261 returns to step S3903.

In step S3905, in a case where the correspondence table is created forall output axis, the scale calculation unit 261 ends the processing.

The processing of the score distribution calculation unit 260 and thescale calculation unit 261 of the present embodiment will be describedin details with reference to FIGS. 40A and 40B. FIGS. 40A and 40B arediagrams describing correspondence between a score and a slider value ofthe sixth embodiment. FIG. 40A is an example histogram, and FIG. 40B isan example table illustrating the association between the score and theslider value.

In FIG. 40A, the horizontal axis indicates the score of the output axis“premier”, and the vertical axis indicates the number of document IDsthat the result of vector matching is equal to or greater than thethreshold value.

In the output axis “premier”, as is known from FIG. 40A, the score rangeis from 0 to 10. While the score is 2 to 5, there are many numbers ofcorresponding search result set data, but in a case where the score is 8or higher or less than 2, there is almost no corresponding search resultset data.

Therefore, in the present embodiment, in a case where the output axis“premier” is displayed as scale, the slider value (graduation) and thenumber of document IDs are associated with each other.

FIG. 40B is an example of a correspondence table related to the outputaxis “premier”. In this case, the slider value “0” and the score “0 to3.5”, the slider value “1” and the score “3.5 to 3.8”, and the slidervalue “2” and the score “3.8 to 4.6” are associated with each other.

For example, in the scale of the output axis “premier”, in a case wherethe slider is at the position of the slider value “0”, document datacorresponding to the document ID in which the score of the impressionword “premier” is 0 to 3.5 among the extracted search result set data isdisplayed. In the scale of the output axis “premier”, when the slider isat a position of the slider value “1”, document data corresponding tothe document ID in which the score of the impression word “premier” is3.5 to 3.8 among the extracted search result set data is displayed.

As described above, in the present embodiment, by associating the slidervalue attached on the scale of the output axis and the variation rangeof the score, regardless of which slider value the slider is stopped onthe scale, it is possible to equalize the number of output search resultset data pieces. In the present embodiment, the association is performedfor every output axes, and stored as the correspondence table 410.

FIG. 41 is an example correspondence table of the sixth embodiment. Inthe correspondence table 410 illustrated in FIG. 41, the slider valueand the variation range of the score are associated with each other foreach output axis “premier”, “hideaway”, and “healthy”.

In the correspondence table 410, with respect to the slider value “0”,the output axis “premier” is associated with the score “0 to 3.5”, theoutput axis “hideaway” is associated with the score “0 to 2.8”, and theoutput axis “healthy” is associated with the score “0 to 2.9”.

In the correspondence table 410, with respect to the slider value “1”,the output axis “premier” is associated with the score “3.5 to 3.8”, theoutput axis “hideaway” is associated with the score “2.8 to 3.6”, andthe output axis “healthy” is associated with the score “2.9 to 3.2”.

FIG. 42 is a diagram illustrating a display example of the sixthembodiment. A screen 421 illustrated in FIG. 42 is displayed in, forexample, the terminal device 300 and the like.

On the screen 421, an input field 422 and a result display field 423 aredisplayed. In the input field 422, a query input field 424 to input thequery, a search button 425 that performs search request, and a pluralityof scales 426 associated with each output axis are displayed. On theplurality of scales 426, a slider 426 a is displayed respectively, andthe score of the corresponding output axis is changed by moving theslider on the scale.

In the result display field 423, the document data specified based onthe input query in the query input field 424 is displayed as the searchresult set data. In the example of FIG. 42, search result set data 427,428, and 429 is displayed as the search result.

In the present embodiment, on at least one of the plurality of scales426, when the slider 426 a is operated and the score is changed, thesearch result set data displayed in the result display field 423 changesas the score changes.

As described above, in the present embodiment, when changing the scoreof the impression word estimated from the search word group based on thequery and performing the search, it is possible to display theappropriate number of search result set data pieces according to thechange in the score of the impression word.

In the present embodiment, as a display mode for changing the score ofthe impression word, a mode in which the score is changed on the scaleto which the slider value is attached is described, but the display modeis not limited this. In the present embodiment, as long as it is a modethat receives the change in score, and displays the search result setdata corresponding to the changed score, any display mode may be used.For example, it may be a mode that an input field to input score isprovided, and the change in the score of the impression word is receivedby input of the score with respect to the input field.

In the present embodiment, in a case where there is a deviation in thescore distribution, the range selected by the slider may be limited, orthe display mode of the scale may be displayed in a hue matching withthe score distribution.

FIGS. 43A and 43B are diagrams illustrating another display example ofthe sixth embodiment. In FIG. 43A, a display example of the slider inwhich the range in which the score is selected by the slider is limitedis illustrated, and FIG. 43B is a display example in a case where thescale is displayed in a hue matching with the score distribution.

In FIG. 43A, a scale 431-1 corresponding to the output axis “premier”, ascale 431-2 corresponding to the output axis “hideaway”, and a scale431-3 corresponding to the output axis “healthy” are displayed.

The scale 431-1 includes an unselectable area 432-1 that is notselectable by the slider, and a selectable area 433-1 that is selectableby the slider. In this case, the scale 431-1 indicates that there is nosearch result set data in the score range indicated by the unselectablearea 432-1, and the search result set data is distributed in the scorerange indicated by the selectable area 433-1.

In this case, even if any position of the selectable area 433-1 isselected by the slider, the scale calculation unit 261 may set thenumber of output search result set data pieces to be the same, orsubstantially the same.

Similarly, the scale 431-2 includes an unselectable area 432-2 and aselectable area 433-2, and the scale 431-3 includes an unselectable area432-3 and a selectable area 433-3.

In the scales 431-1, 431-2, and 431-3, the unselectable areas 432-1,432-2, and 432-3 may be displayed in a different manner from theselectable areas 433-1, 433-2, and 433-3.

In the present embodiment, as described in FIG. 43A, it is possible tomake the user to visually recognize that the range in which the score isselected by the slider is limited depending on the display mode of thescale.

In FIG. 43B, a scale 434-1 corresponding to the output axis “premier”, ascale 434-2 corresponding to the output axis “hideaway”, and a scale434-3 corresponding to the output axis “healthy” are displayed.

In the example of FIG. 43B, the selectable areas 435-1, 435-2, and 435-3in each scale are displayed with red density being increased in orderfrom an area where the number of search result set data pieces is high.

In the present embodiment, by displaying the scale in this way, it ispossible to make the user to visually recognize the score range wherethe number of the search result set data pieces is large. In this case,the user may select the dark red area with the slider in a case wherethe user desires to display other search result set data.

As described above, according to the present embodiment, even in a casewhere the impression word as an item displaying the search result setdata, and the score indicating the strength of the relationship with thequery are changed, it is possible to output the appropriate number ofsearch result set data pieces.

Seventh Embodiment

Hereinafter, the seventh embodiment will be described with reference tothe drawings. The seventh embodiment is different from the sixthembodiment in that the range of the score associated with the slidervalue is limited. In the description of the seventh embodiment below,only the differences from the sixth embodiment will be described. Thosehaving the same functional configuration as those of the sixthembodiment are denoted by the same reference numerals as those used inthe description of the sixth embodiment, and descriptions thereof willbe omitted.

FIG. 44 is a graph describing correspondence between a score and aslider value of a seventh embodiment.

In FIG. 44, the horizontal axis indicates the score of the output axis“premier”, and the vertical axis indicates the number of document IDswhere the result of vector matching is equal to or greater than thethreshold value. In the example of FIG. 44, the initial value of thescore of the output axis “premier” is “6”. The initial value is thescore of the impression word “premier” corresponding with the documentID with the strongest relationship with the query.

In the example of FIG. 44, the slider value and the variation range ofthe score are associated with each other within a predetermined range Hcentered on the initial value as the score range. The predeterminedrange H may be set in advance.

For example, in the present embodiment, the range of the score “4 to 8”is expressed on the scale with the minimum value of the score being “4”and the maximum value being “8”

FIG. 45 is a flowchart describing processing of a search processing unitof the seventh embodiment. The scale calculation unit 261 of the presentembodiment acquires the score range (from minimum score to maximumscore), the histogram, and the X number of document IDs acquired whenthe histogram is generated for each output axis (step S4501).

The scale calculation unit 261 acquires the Y number of search resultset data pieces corresponding to the predetermined score range set inadvance for each output axis (step S4502).

Since the processing of step S4503 and step S4504 of FIG. 45 are similarto the processing of step S3902 and step S3903 of FIG. 39, descriptionthereof is omitted.

The scale calculation unit 261 creates a correspondence table so thatthe number of document IDs corresponding to the slider value is set tobe Y/N for the selected output axis (step S4505).

Since the processing of step S4506 of FIG. 45 is similar to theprocessing of step S3905 of FIG. 39, description thereof is omitted.

FIG. 46 is an example correspondence table of the seventh embodiment. Ina correspondence table 460 illustrated in FIG. 46, the score 4 to 8 isassociated with the slider value 0 to 5.

In the present embodiment, in the scale of the output axis “premier”,the search result set data with score 4 or less or 8 or higher is notoutput even if the slider value is moved from 0 to 5.

In the present embodiment, as described above, by limiting the range ofthe score associated with the slider value, the output of the searchresult set data with a low relationship with the input query may besuppressed.

In the disclosed technology, a mode such as that described below may beconsidered.

The present embodiments are not limited to the specifically disclosedembodiments, and various modifications and changes may be made withoutdeparting from the scope of the claims.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A search method by a computer, comprising:specifying a search word group based on a query; acquiring search resultset data using the search word group from search target data; extractinga first feature word group based on the query; displaying the searchresult set data using the first feature word group as an item;acquiring, for each word included in the search word group, a valueindicating a strength of a relationship between each impression wordincluded in an impression word group and the word, and a valueindicating a variation of the value indicating the strength of therelationship; notifying a third feature word group as a candidate of thefirst feature word group in a case where the impression word having thevalue indicating the variation larger than a threshold is included inthe first feature word group; and displaying the impression word havingthe value indicating the variation larger than the threshold and thethird feature word group in association with each other.
 2. The searchmethod according to claim 1, further comprising: extracting a secondfeature word group from the search result set data; and displaying thesearch result set data using the first feature word group and the secondfeature word group as the item.
 3. The search method according to claim1, further comprising: converting a word included in the search wordgroup to a vector by a distributed representation; acquiring a valueindicating a strength of a relationship between each impression word andeach word included in the search word group by a conversion model thatoutputs an impression word group and the value indicating the strengthof the relationship between each impression word included in theimpression word group and the word when the vector is input; andextracting the first feature word group according to the valueindicating the strength of the relationship with each word, from theimpression word group.
 4. The search method according to claim 3,wherein the first feature word group has a predetermined number ofimpression words extracted in descending order of the impression wordshaving a maximum value of the value indicating the strength of therelationship with each word among impression words included in theimpression word group.
 5. The search method according to claim 3,further comprising extracting the second feature word group other thanthe first feature word group from the impression word group, anddisplaying the search result set data using the first feature word groupand the second feature word group as the item.
 6. The search methodaccording to claim 5, further comprising: extracting the impression wordhaving a smaller maximum value of the value indicating the strength ofthe relationship with each word than the impression word extracted asthe first feature word group among the impression words included in theimpression word group; and using the predetermined number of impressionwords extracted in descending order of the impression words havingmaximum dispersion of the value indicating the strength of therelationship with each word among the extracted impression words as thesecond feature word group.
 7. The search method according to claim 3,further comprising: specifying a word group included in document datafor each document data of the search target data; acquiring the valueindicating the strength of the relationship between each impression wordand each word, based on the vector converted from the word included inthe word group and the conversion model; acquiring a value indicating astrength of a relationship between the document data and each impressionword from the value indicating the strength of the relationship betweeneach impression word and each word; and when the document data isacquired as the search result set data, referring to the valueindicating the strength of the relationship between the document dataand each impression word, and displaying the value indicating thestrength of the relationship between the document data and theimpression word as the first feature word group, as a value of the item.8. The search method according to claim 7, wherein a value indicatingthe variation is a standard deviation of the value indicating thestrength of the relationship, and wherein the third feature word grouphas impression words other than the first feature word group among theimpression word group, and has the impression words in which the valueindicating the strength of the relationship is a value larger than avalue obtained by subtracting the standard deviation from the valueindicating the strength of the relationship with the impression wordhaving the standard deviation larger than a predetermined threshold. 9.The search method according to claim 8, further comprising: receiving aselection of the impression word included in the third feature wordgroup; and displaying the search result set data using an impressionword having the standard deviation equal to or less than thepredetermined threshold among the first feature word group, and theimpression word selected from the third feature word group as the item.10. The search method according to claim 9, wherein, in the firstfeature word group, in a case where there is an impression wordspecified to maintain display, the specified impression word and theimpression word selected from the third feature word group are includedin the item.
 11. The search method according to claim 10, furthercomprising: receiving an operation that specifies the impression wordfor which display is maintained, and storing the impression word forwhich the display is maintained and a first correction value thatcorrects a value indicating the strength of the relationship with eachword in a storage unit in association with each other; and receiving anoperation that selects the impression word from the third feature wordgroup, and storing the impression word selected from the third featureword group and a second correction value that corrects the valueindicating the strength of the relationship with each word in thestorage unit in association with each other.
 12. The search methodaccording to claim 3, further comprising: for each impression wordextracted as the first feature word group, calculating distribution ofthe value indicating the strength of the relationship between the searchresult set data and the impression word with respect to search resultset data pieces; and displaying a scale indicating the strength of therelationship with the impression word based on the distribution so thatthe number of the search result set data pieces corresponding to avariation range of the value indicating the strength of the relationshipwith the impression word is a predetermined number.
 13. The searchmethod according to claim 12, wherein, on the scale, a slider thatreceives a change in the value indicating the strength of therelationship with the impression word is displayed with the scale, andwherein a position where the slider stops and the variation range of thevalue indicating the strength of the relationship with the impressionword are associated with each other on the scale.
 14. The search methodaccording to claim 13, further comprising: setting a maximum value ofthe value indicating the strength of the relationship with each word asan initial value; and displaying the slider at a position correspondingwith the initial value on the scale.
 15. The search method according toclaim 12, further comprising based on the distribution, associating apart of a range of the value indicating the strength of the relationshipwith the impression word with the number of the search result set datapieces.
 16. An apparatus comprising: a memory; and a processor coupledto the memory and configured to: specify a search word group based on aquery, acquire search result set data using the search word group fromsearch target data, extract a first feature word group based on thequery, display the search result set data using the first feature wordgroup as an item, acquire, for each word included in the search wordgroup, a value indicating a strength of a relationship between eachimpression word included in an impression word group and the word, and avalue indicating a variation of the value indicating the strength of therelationship; notify a third feature word group as a candidate of thefirst feature word group in a case where the impression word having thevalue indicating the variation larger than a threshold is included inthe first feature word group; and display the impression word having thevalue indicating the variation larger than the threshold and the thirdfeature word group in association with each other.
 17. Acomputer-implemented search method, comprising: receiving, via an inputdevice, query input data including a word or a phrase; acquiring searchresult set data using the query input data; acquiring, for a valueindicating a strength of a relationship between each impression wordincluded in an impression word group and each word included in the queryinput data; extracting the first feature word group according to thevalue indicating the strength of the relationship with each word, fromthe impression word group; displaying the search result set data usingthe first feature word group as an item, acquiring, for each wordincluded in the search word group, a value indicating a strength of arelationship between each impression word included in an impression wordgroup and the word, and a value indicating a variation of the valueindicating the strength of the relationship; notifying a third featureword group as a candidate of the first feature word group in a casewhere the impression word having the value indicating the variationlarger than a threshold is included in the first feature word group; anddisplaying the impression word having the value indicating the variationlarger than the threshold and the third feature word group inassociation with each other.
 18. The computer-implemented search methodaccording to claim 17, further comprising: acquiring a value indicatinga variation of the value indicating the strength of the relationship;and notifying a second feature word group as a candidate of the firstfeature word group when the impression word having the value indicatingthe variation larger than a threshold is included in the first featureword group.
 19. The computer-implemented search method according toclaim 18, further comprising: displaying the impression word having thevalue indicating the variation larger than the threshold and the secondfeature word group in association with each other.